检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Zhang, Qin Liang, Zhuoluo Ngueilbaye, Alladoumbaye Zhang, Peng Chen, Junyang Chen, Xiaojun Huang, Joshua Zhexue College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

The recent emergence of time series contrastive clustering methods can be categorized into two classes. The first class uses contrastive learning for universal representations, which can be effective in various downstream tasks, but overlooks important categorical information and clustering objectives, leading to unsuitable models. The second class incorporates contrastive learning into clustering objectives but fails to fully explore potential connections and structures between time series data. To this end, we propose the "Time Series Graph-augmented Contrastive Clustering (TSGCC) method", a graph-augmented contrastive learning framework aiming to create cluster-friendly features and assignments by iteratively optimizing contrastive loss. The method uses a weighted KNN graph to build positive and negative sample pairs, projecting the instances into a feature space with the dimensionality number of clusters. Experimental results demonstrate that TSGCC outperforms 18 advanced time-series clustering methods on the 36 representatives out of the 128 UCR benchmarks, achieving the best clustering results on 12 datasets, with the highest average rank (3.22) of RI and the second-highest average rank (3.89) of NMI among 18 clustering methods. We further compared our method with recent approaches on the entire set of 128 UCR datasets, demonstrating our method's broad applicability and effectiveness. © 2024, The Authors. All rights reserved.

关键词： Time series

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Multi-Instance Multi-Label Classification from Crowdsourced Labels 39

Multi-Instance Multi-Label Classification from Crowdsourced ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Ziquan Xia, Mingxuan Ren, Xiangyu Zhou, Jiaqing Lyu, Gengyu Hu, Tianlei Wang, Haobo School of Software Technology Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China College of Computer and Control Engineering Northeast Forestry University China ByteDance Hangzhou China College of Computer Science Beijing University of Technology China

ISBN: (纸本)157735897X

Multi-instance multi-label classification (MIML) is a fundamental task in machine learning, where each data sample comprises a bag containing several instances and multiple binary labels. Despite its wide applications, the data collection process involves matching multiple instances and labels, typically resulting in high annotation costs. In this paper, we study a novel yet practical crowdsourced multi-instance multi-label classification (CMIML) setup, where labels are collected from multiple crowd sources. To address this problem, we first propose a novel data generation process for CMIML, i.e., cross-label transition, where cross-label annotation error is more likely to appear rather than previous single-label transition assumption, due to the inherent similarity of localized instances from different classes. Then, we formally define the cross-label transition by cross-label transition matrices which are dependent across classes. Subsequently, we establish the first unbiased risk estimator for CMIML and further improve it through aggregation techniques, along with a rigorous generalization error bound. We also provide a practical implementation of cross-label transition matrix estimation. Comprehensive experiments on six benchmark datasets under various scenarios demonstrate that our algorithm outperforms the baselines by a large margin, validating its effectiveness in handling the CMIML problem. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Nameplates

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Assessing the Robustness of LLM-based NLP software via Automated Testing

arXiv

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arXiv 2024年

作者： Xiao, Mingxuan Xiao, Yan Ji, Shunhui Cai, Hanbo Xue, Lei Zhang, Pengcheng Key Laboratory of Water Big Data Technology Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing211100 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China

Benefiting from the advancements in LLMs, NLP software has undergone rapid development. Such software is widely employed in various safety-critical tasks, such as financial sentiment analysis, toxic content moderation, and log generation. Unlike traditional software, LLM-based NLP software relies on prompts and examples as inputs. Given the complexity of LLMs and the unpredictability of real-world inputs, quantitatively assessing the robustness of such software is crucial. However, to the best of our knowledge, no automated robustness testing methods have been specifically designed to evaluate the overall inputs of LLM-based NLP software. To this end, this paper introduces the first AutOmated Robustness Testing frAmework, AORTA, which reconceptualizes the testing process into a combinatorial optimization problem. Existing testing methods designed for DNN-based software can be applied to LLM-based software by AORTA, but their effectiveness is limited. To address this, we propose a novel testing method for LLM-based software within AORTA called Adaptive Beam Search. ABS is tailored for the expansive feature space of LLMs and improves testing effectiveness through an adaptive beam width and the capability for backtracking. We successfully embed 18 test methods in the designed framework AORTA and compared the test validity of ABS with three datasets and five threat models. ABS facilitates a more comprehensive and accurate robustness assessment before software deployment, with an average test success rate of 86.138%. Compared to the currently best-performing baseline PWWS, ABS significantly reduces the computational overhead by up to 3441.895 seconds per successful test case and decreases the number of queries by 218.762 times on average. Furthermore, test cases generated by ABS exhibit greater naturalness and transferability. © 2024, CC BY-NC-ND.

关键词： software testing

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Listwise ranking predictor for evolutionary neural architecture search

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Swarm and Evolutionary Computation 2025年 96卷

作者： Li, Nan Ma, Lianbo Wang, Rui Cheng, Shi Sun, Yanan Xue, Bing Zhang, Mengjie College of Software Northeastern University Liaoning Shenyang110819 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington 6140 New Zealand Foshan Graduate School of Innovation Guangdong Foshan528000 China College of System Engineering National University of Defense Technology Hunan Changsha410073 China School of Computer Science Shaanxi Normal University Shaanxi Xian710119 China School of Computer Science Sichuan University Sichuan Chengdu610000 China

In evolutionary neural architecture search (ENAS), the accuracy predictors (i.e., regression models) have been successfully applied to save computational costs for the evaluation of network architectures. However, the accuracy of these predictors is largely limited by the small amount of evaluated architectures that may be difficult to obtain. Such accuracy predictors with prediction bias often lead to an inaccurate ranking, misleading the selection of ENAS. To alleviate the above limitations, we design an efficient and novel listwise ranking predictor (LRP) for ENAS to directly predict the ranking of each architecture instead of the numerical accuracy value of each architecture. Specifically, the training data is constructed by the proposed random encoding-combination (REC) strategy, which can generate substantial training data using the small number of evaluated architectures (data level). These specially constructed training data are used to train LRP, which can convert the complex regression task into a ranking task to reduce ranking bias (model level). The proposed NAS method is compared with state-of-the-art NAS methods on widely-used benchmark datasets and practical application. Experimental results demonstrate that LRP can alleviate the ranking disorder problem and outperform others in terms of both effectiveness and efficiency. © 2025 Elsevier B.V.

关键词： Linear regression

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Collaborative Segmentation Model for Colonoscopy Ulcers based on Fuzzy Labeling*

Collaborative Segmentation Model for Colonoscopy Ulcers base...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Yanning Lin Jie Chen Ziqian Ding Linxu Chen Le Li Zhongsheng Zhu Zhaoxia Wang Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Children’s Hospital Shenzhen China Gastroenterology Shenzhen Children’s Hospital China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Inflammatory Bowel Disease (IBD) is a global chronic intestinal inflammatory disease, and its incidence rate increases year by year with the progress of economic globalization. Currently, the diagnosis of IBD in children mainly relies on endoscopic examination, but scoring endoscopic images is a challenging issue, especially in distinguishing different types of ulcers. To address this issue, this article designs a mobile application to accelerate data annotation processing and may provide reference for other unlabeled datasets. In the context of image segmentation, blurring labels has become an important issue. Deep learning methods are widely used in medical image segmentation, but their accuracy depends on high-quality annotated data. However, there are low-quality noise areas in the annotated data, and obtaining accurate and high-quality annotations becomes more time-consuming with limited annotation budgets. This article proposes a collaborative training framework to improve learning of noisy pixels. This framework determines the label confidence of an image by calculating the similarity between image pixels and surrounding pixels. Then, two parallel deep networks were constructed for semantic prediction, which aimed to guide each other on pixels that may have noise. By applying consistency in dual network prediction, the semantic information of uncertain pixels is corrected as much as possible. Experimental results have shown that this framework is slightly superior to models trained with pixel level precise labels, thus more effectively utilizing existing annotated data in the case of fuzzy labels.

关键词： Training Image segmentation Accuracy Annotations Biological system modeling Semantics Noise Collaboration Colonoscopy Medical diagnostic imaging

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

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5th IFIP TC 12 International Conference on Intelligence science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jie Chen Wenbin Liao Du Wen Jianqiang Li Fangzhong Wang College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology Tianjin University China

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model’s robustness.

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WMAJL: Watcher-Mediated Attention Joint Learning Model for Multimodal Relation Extraction

WMAJL: Watcher-Mediated Attention Joint Learning Model for M...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Deep Learning-based Image Text Processing Research*

Deep Learning-based Image Text Processing Research*

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IEEE International Conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart Computing (HPSC) and Intelligent data and Security (IDS)

作者： Huixuan Xiong Kai Jin Jingnian Liu Jiahong Cai Lijun Xiao School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China the College of Computer Science and Electronics Engineering Hunan University Changsha China Big Data Development and Research Center Guangzhou College of Technology and Business Guangzhou China

Deep learning is a powerful multi-layer architecture that has important applications in image processing and text classification. This paper first introduces the development of deep learning and two important algorithms of deep learning: convolutional neural networks and recurrent neural networks. The paper then introduces three applications of deep learning for image recognition, image detection, and image forensics, as well as three text classification methods based on convolutional neural networks, recurrent neural networks, and other text classification methods. Finally, the development trend of deep learning in the field of text and image processing and the difficulties to be further researched are summarised and prospected.

关键词： Deep learning Image forensics Recurrent neural networks Computational modeling Text categorization Feature extraction data models

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